{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "fig = plt.figure(figsize=(6,3)) # Create matplotlib figure\n",
    "\n",
    "instruct_data = (102 +  90 + 50 + 158)/1000.0\n",
    "\n",
    "# Create sample data  \n",
    "x = np.array([1, 2, 3, 4, 5])  \n",
    "\n",
    "y1 = np.array([0, 0, 0.158, 0.0035, instruct_data])  \n",
    "y2 = np.array([129, 15, 0.595, 5, 1112])  \n",
    "model_names = ['BLIP2', 'OpenFlamingo', 'LLaVA', 'MiniGPT4', 'mPLUG-Owl']\n",
    "\n",
    "for xx, yy1 in zip(x, y1):\n",
    "    if yy1 != 0:\n",
    "        plt.text(xx-.3, 0.005, f'{yy1*1000}K', color='k', fontsize=10)\n",
    "\n",
    "\n",
    "# Add text on top of each bar  \n",
    "for i, v in enumerate(y2):  \n",
    "    plt.text(i+0.7, v+1, str(v)+'M', color='k', fontsize=10)  \n",
    "\n",
    "# Plot the first set of bars  \n",
    "plt.bar(x, y1, color='orange', label='Instruction-following data')  \n",
    "  \n",
    "# Plot the second set of bars on top of the first set  \n",
    "plt.bar(x, y2, color='skyblue', bottom=y1, tick_label=model_names, label='Image-text pairs')  \n",
    "  \n",
    "# Add labels and title  \n",
    "plt.ylabel('# Training samples (Millions)')  \n",
    "plt.yscale('log') \n",
    "  \n",
    "plt.legend() \n",
    "# Show the plot  \n",
    "plt.show()  \n",
    "\n",
    "fig.savefig('output/barchart_training_data.pdf', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bad pipe message: %s [b'\\x1f\\x1dT\\xab\\x16p\\xa2R\\x8a\\xa9\\xaaj\\xe3S\\xd7z\\x9bU \\x99@\\x11\\x06\\xa8~G\\xcb\\xc4l\\x10\\x0eP\\xbc\\x89\\xea\\xdc\\x14\\x8d&\\x91\\xac\\x86\\xa8\\x02s\\xbc)\\xa2r\\xcd\\xf4\\x00\\x08\\x13\\x02\\x13\\x03\\x13\\x01\\x00\\xff\\x01\\x00\\x00\\x8f\\x00\\x00\\x00\\x0e\\x00\\x0c\\x00\\x00\\t127.0.0.1\\x00\\x0b\\x00\\x04\\x03\\x00\\x01\\x02\\x00\\n\\x00\\x0c\\x00\\n\\x00\\x1d\\x00\\x17\\x00\\x1e\\x00\\x19\\x00\\x18\\x00#\\x00\\x00\\x00\\x16\\x00\\x00\\x00\\x17\\x00\\x00\\x00\\r\\x00\\x1e\\x00\\x1c\\x04\\x03\\x05\\x03\\x06\\x03\\x08\\x07\\x08\\x08\\x08\\t\\x08\\n\\x08\\x0b\\x08\\x04\\x08\\x05\\x08\\x06\\x04\\x01\\x05\\x01\\x06\\x01\\x00+\\x00\\x03\\x02\\x03\\x04\\x00-\\x00\\x02\\x01\\x01\\x003\\x00&']\n",
      "Bad pipe message: %s [b\"\\xa3\\x81\\xb3\\xf9\\x02Y\\xc4\\xf1\\xeb6c@\\xba\\xae\\xe3\\x8d0%\\x00\\x00|\\xc0,\\xc00\\x00\\xa3\\x00\\x9f\\xcc\\xa9\\xcc\\xa8\\xcc\\xaa\\xc0\\xaf\\xc0\\xad\\xc0\\xa3\\xc0\\x9f\\xc0]\\xc0a\\xc0W\\xc0S\\xc0+\\xc0/\\x00\\xa2\\x00\\x9e\\xc0\\xae\\xc0\\xac\\xc0\\xa2\\xc0\\x9e\\xc0\\\\\\xc0`\\xc0V\\xc0R\\xc0$\\xc0(\\x00k\\x00j\\xc0#\\xc0'\\x00g\\x00@\\xc0\\n\\xc0\\x14\\x009\\x008\\xc0\\t\\xc0\\x13\\x003\\x002\\x00\\x9d\\xc0\\xa1\\xc0\\x9d\\xc0Q\\x00\\x9c\\xc0\\xa0\\xc0\\x9c\\xc0P\\x00=\\x00<\\x005\\x00/\\x00\\x9a\\x00\\x99\\xc0\\x07\\xc0\\x11\\x00\\x96\\x00\\x05\\x00\\xff\\x01\\x00\\x00j\\x00\\x00\\x00\\x0e\\x00\\x0c\\x00\\x00\\t127.0.0.1\\x00\\x0b\\x00\\x04\\x03\\x00\\x01\\x02\\x00\\n\\x00\\x0c\\x00\\n\\x00\\x1d\\x00\\x17\\x00\\x1e\\x00\\x19\\x00\\x18\\x00#\\x00\\x00\\x00\\x16\\x00\\x00\\x00\\x17\\x00\\x00\\x00\\r\\x000\\x00.\\x04\\x03\\x05\\x03\\x06\\x03\\x08\\x07\\x08\\x08\\x08\\t\\x08\\n\\x08\\x0b\\x08\\x04\\x08\\x05\\x08\", b'\\x01\\x05\\x01\\x06\\x01']\n",
      "Bad pipe message: %s [b'\\x02\\x03', b'\\x02\\x01', b'\\x02\\x02']\n",
      "Bad pipe message: %s [b'\\x05\\x02\\x06']\n",
      "Bad pipe message: %s [b'\\xc2\\x06\\xda\\xfd&\\xbe\\xd9H\\xb28\\xcd\\xf60T\\xb2v\\\\7\\x00\\x00>\\xc0\\x14\\xc0\\n\\x009\\x008\\x007\\x006\\xc0\\x0f\\xc0\\x05\\x005\\xc0\\x13\\xc0\\t\\x003\\x002\\x001\\x000\\xc0\\x0e\\xc0\\x04\\x00/\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00\\x96\\x00\\x07\\xc0\\x11\\xc0\\x07\\xc0\\x0c\\xc0\\x02\\x00\\x05\\x00\\x04\\x00\\xff\\x02\\x01\\x00\\x00C\\x00\\x00\\x00\\x0e\\x00']\n",
      "Bad pipe message: %s [b'\\x00\\t127.0.0.1']\n",
      "Bad pipe message: %s [b'\\x14\\x07\\xcfIC\\xccTv\\xbbM\\xc2\\xda\\x8d\\xfa\\x82W(\\xf8\\x00\\x00\\xa2\\xc0\\x14\\xc0\\n\\x009\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00:\\x00\\x89\\xc0\\x0f\\xc0\\x05\\x005\\x00\\x84\\xc0\\x13\\xc0\\t\\x003\\x002\\x001\\x000\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00E\\x00D\\x00C\\x00B\\xc0\\x18\\x004\\x00\\x9b\\x00F\\xc0\\x0e\\xc0\\x04\\x00/\\x00\\x96\\x00A\\x00\\x07\\xc0\\x11\\xc0\\x07\\xc0\\x16\\x00\\x18\\xc0\\x0c\\xc0\\x02\\x00\\x05\\x00\\x04\\xc0\\x12\\xc0\\x08\\x00\\x16\\x00\\x13\\x00\\x10\\x00\\r\\xc0\\x17\\x00\\x1b\\xc0\\r\\xc0\\x03\\x00\\n\\x00\\x15\\x00\\x12\\x00\\x0f\\x00\\x0c\\x00\\x1a\\x00\\t\\x00\\x14\\x00\\x11\\x00\\x19\\x00\\x08\\x00\\x06\\x00\\x17\\x00\\x03\\xc0\\x10\\xc0\\x06\\xc0\\x15\\xc0\\x0b\\xc0\\x01\\x00\\x02\\x00\\x01\\x00\\xff\\x02\\x01\\x00\\x00C\\x00\\x00\\x00\\x0e\\x00\\x0c\\x00\\x00\\t127.0.0.1\\x00\\x0b\\x00\\x04\\x03\\x00\\x01\\x02\\x00\\n\\x00\\x1c\\x00\\x1a\\x00\\x17\\x00\\x19\\x00\\x1c\\x00\\x1b\\x00\\x18\\x00\\x1a\\x00\\x16\\x00\\x0e\\x00\\r\\x00\\x0b\\x00\\x0c\\x00\\t\\x00', b'#\\x00\\x00\\x00\\x0f\\x00\\x01\\x01\\x15']\n",
      "Bad pipe message: %s [b'FCb\\xbe:1PW5\\xad\\x04p\\xc9\\xe9=\\xdb\\x18$\\x00\\x00\\xa2\\xc0\\x14\\xc0\\n\\x009\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00:\\x00\\x89\\xc0\\x0f\\xc0\\x05\\x005\\x00\\x84\\xc0\\x13\\xc0\\t\\x003\\x002\\x001\\x000\\x00\\x9a']\n",
      "Bad pipe message: %s [b'\\xae&\\xafN\\xc6\\xce\\xeb\\xb9\\xc6\\xf7\\x82R!\\xb2\\x14\\r%h\\x00\\x00\\xf4\\xc00\\xc0,\\xc0(\\xc0$\\xc0\\x14\\xc0\\n\\x00\\xa5\\x00\\xa3\\x00\\xa1\\x00\\x9f\\x00k\\x00j\\x00i\\x00']\n",
      "Bad pipe message: %s [b\"9\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00\\xa7\\x00m\\x00:\\x00\\x89\\xc02\\xc0.\\xc0*\\xc0&\\xc0\\x0f\\xc0\\x05\\x00\\x9d\\x00=\\x005\\x00\\x84\\xc0/\\xc0+\\xc0'\\xc0#\\xc0\\x13\\xc0\\t\\x00\\xa4\\x00\\xa2\\x00\\xa0\\x00\\x9e\\x00g\\x00@\\x00?\\x00>\\x003\\x002\\x001\\x000\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00E\\x00D\\x00C\\x00B\\xc0\\x18\\x00\\xa6\\x00l\"]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "fig = plt.figure(figsize=(12,2)) # Create matplotlib figure\n",
    "\n",
    "# \tChest X-Ray\tMRI\tHistology\tGross\tCT Scan\tOverall\n",
    "# Conversations/Images\t8060\t16539\t17030\t2552\t17358\t60100\n",
    "# Turns\t22839\t47867\t49504\t7323\t50583\t344614\n",
    "\n",
    "instruct_data = (102 +  90 + 50 + 158)/1000.0\n",
    "\n",
    "# Create sample data  \n",
    "x = np.array([1, 2, 3, 4, 5]) * 2 \n",
    "\n",
    "y1 = np.array([8060,\t16539,\t17030,\t2552,\t17358])  \n",
    "y2 = np.array([22839,\t47867,\t49504,\t7323,\t50583])  \n",
    "model_names = ['Chest', 'X-Ray', 'MRI', 'Histology', 'Gross']\n",
    "\n",
    "for xx, yy1 in zip(x, y1):\n",
    "    if yy1 != 0:\n",
    "        plt.text(xx-.3, 3000, f'{yy1}', color='k', fontsize=10)\n",
    "\n",
    "\n",
    "# Add text on top of each bar  \n",
    "for xx, v in zip(x, y2):\n",
    "    plt.text(xx-.3, v+4000, str(v), color='k', fontsize=10)  \n",
    "\n",
    "# Plot the first set of bars  \n",
    "plt.bar(x, y1, color='orange', label='# Images / Conversation', width=0.6)  \n",
    "  \n",
    "# Plot the second set of bars on top of the first set  \n",
    "plt.bar(x, y2, color='skyblue', bottom=y1, tick_label=model_names, label='# QA Pairs / Turns', width=0.6)  \n",
    "  \n",
    "# Add labels and title  \n",
    "plt.ylabel('# Instances')  \n",
    "# plt.yscale('log') \n",
    "\n",
    "# X-axis label\n",
    "plt.yticks( (0.0, 20000, 40000, 60000), ('0', '20K','40K','60K'), color='k', size=10)\n",
    "plt.xlim(1.5, 12)   \n",
    "plt.ylim(0, 80000)  \n",
    "plt.legend() \n",
    "\n",
    "leg = plt.legend(fontsize=10, shadow=True, loc=(0.25, 0.9),  ncol=2)\n",
    "\n",
    "\n",
    "# Show the plot  \n",
    "plt.show()  \n",
    "\n",
    "fig.savefig('output/barchart_instruc_data_inline_mentions.pdf', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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